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# Jet Classification Using Deep Learning
This project demonstrates the process of using deep learning techniques to classify jet images from high-energy physics experiments.
## Dataset
The dataset used for this analysis is available in `.h5` format and consists of multiple files. Each file contains jet data with various features and corresponding labels. The script processes a selection of these files to extract relevant features and targets for training.
## Data Preparation
### Loading Libraries
The code begins by importing required libraries such as `h5py` for handling HDF5 files, `numpy` for numerical operations, and `matplotlib` for plotting.
### Cloning the Dataset
The code attempts to clone the dataset from a GitHub repository. If the repository already exists, it skips this step.
### Defining Targets and Features
The script initializes empty numpy arrays for target and features. It then specifies the files to be processed.
### Appending Data
For each specified file, it opens the HDF5 file, extracts the features and targets, and appends them to the respective arrays. The features include 16 attributes related to the jets, while the targets contain labels for each jet.
### Data Shape
After processing, the shapes of the features and targets arrays are printed to confirm the dimensions (50,000 jets, each with 16 features).
## Data Splitting
The dataset is split into training and validation sets using `train_test_split` from `scikit-learn`. The training set contains 67% of the data, while 33% is reserved for validation. This split ensures the model is evaluated on unseen data during training.
## Model Building
### Model Architecture
A Dense Neural Network is constructed using Keras. The model consists of:
- An input layer with shape corresponding to the feature size.
- Several dense layers with ReLU activation functions.
- Dropout layers to prevent overfitting.
- An output layer using softmax activation to output probabilities for the 5 jet classes (gluon, quark, W, Z, top).
### Compilation
The model is compiled with categorical cross-entropy loss and the Adam optimizer.
## Training the Model
The model is trained on the training data for a maximum of 50 epochs. Early stopping, learning rate reduction, and termination on NaN are used as callbacks to enhance training stability and efficiency.
The training progress is printed for each epoch, showing the training and validation loss.
## Evaluation
After training, the model's performance is evaluated using Receiver Operating Characteristic (ROC) curves for each class. The curves are plotted to visualize the trade-off between sensitivity and specificity, and the area under the curve (AUC) is calculated.
## Visualizing Training History
The training and validation loss are plotted on a logarithmic scale to visualize the learning process. This helps in understanding how well the model is converging.
## Conclusion
This project demonstrates the process of using deep learning techniques to classify jet images from high-energy physics experiments. The model can be further refined and evaluated with more advanced techniques and larger datasets.Feel free to customize it further based on your specific needs! If you need any more details or adjustments, let me know.